Pharmacovigilance and Adverse Drug Reaction Patterns: A Data-Driven Study on Drug Safety in Clinical Practice
Keywords:
Pharmacovigilance, adverse drug reaction, data-driven analysis, real-world data, drug safety, signal detection.Abstract
Pharmacovigilance is the systematic process of monitoring and analyzing adverse drug reactions (ADRs) to ensure patient safety after medications reach the market. This paper reviews contemporary data-driven approaches to pharmacovigilance, focusing on how large healthcare databases and novel analytics can reveal ADR patterns. We describe traditional data sources such as spontaneous reporting systems (e.g. the WHO’s VigiBase and FDA’s FAERS) and electronic health records (EHRs), as well as emerging real-world data (RWD) sources like social media and patient registries. Key methods including disproportionality statistics and machine learning are discussed, with examples of recent studies using open ADR datasets. We highlight experiments using publicly available data, such as mining FAERS reports with disproportionality analysis and developing ADR reference sets for validation. For instance, one systematic review notes that in the U.S., ADRs accounted for ~6% of hospital admissions in 2011 and cost billions. We also examine regional patterns (e.g. differences in ADR reporting between Japan and global data) and discuss challenges like underreporting and data quality. Figures and tables illustrate the pharmacovigilance process and ADR data characteristics. By leveraging modern data science, stakeholders can detect safety signals earlier and tailor drug-safety practices to populations worldwide.
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